Overview

Dataset statistics

Number of variables7
Number of observations785
Missing cells0
Missing cells (%)0.0%
Duplicate rows24
Duplicate rows (%)3.1%
Total size in memory65.2 KiB
Average record size in memory85.1 B

Variable types

Numeric6
Categorical1

Alerts

Dataset has 24 (3.1%) duplicate rowsDuplicates
dissimilarity is highly overall correlated with contrast and 4 other fieldsHigh correlation
contrast is highly overall correlated with dissimilarity and 3 other fieldsHigh correlation
homogeneity is highly overall correlated with dissimilarity and 4 other fieldsHigh correlation
energy is highly overall correlated with dissimilarity and 4 other fieldsHigh correlation
ASM is highly overall correlated with dissimilarity and 4 other fieldsHigh correlation
correlation is highly overall correlated with dissimilarity and 4 other fieldsHigh correlation
Label is highly overall correlated with homogeneityHigh correlation

Reproduction

Analysis started2022-12-06 10:15:22.472101
Analysis finished2022-12-06 10:15:32.027877
Duration9.56 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

dissimilarity
Real number (ℝ)

Distinct758
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9239087
Minimum0
Maximum15.950192
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2022-12-06T15:15:32.317512image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7314914
Q17.2047457
median8.0300059
Q38.7627296
95-th percentile9.774329
Maximum15.950192
Range15.950192
Interquartile range (IQR)1.5579839

Descriptive statistics

Standard deviation1.4354195
Coefficient of variation (CV)0.18115044
Kurtosis6.6067881
Mean7.9239087
Median Absolute Deviation (MAD)0.7730954
Skewness-0.76042318
Sum6220.2683
Variance2.0604292
MonotonicityNot monotonic
2022-12-06T15:15:32.480273image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.5%
7.173842001 3
 
0.4%
8.421013201 2
 
0.3%
7.920502984 2
 
0.3%
7.53088429 2
 
0.3%
6.365173474 2
 
0.3%
7.367756852 2
 
0.3%
7.563079646 2
 
0.3%
8.529314888 2
 
0.3%
6.426393087 2
 
0.3%
Other values (748) 762
97.1%
ValueCountFrequency (%)
0 4
0.5%
2.166937921 1
 
0.1%
2.268103025 1
 
0.1%
3.382113979 1
 
0.1%
3.543374926 1
 
0.1%
3.597793795 1
 
0.1%
4.0614392 1
 
0.1%
4.30058901 1
 
0.1%
4.437572658 1
 
0.1%
4.483989511 1
 
0.1%
ValueCountFrequency (%)
15.95019246 1
0.1%
15.54783125 1
0.1%
11.82802449 1
0.1%
11.70057351 1
0.1%
11.33971428 1
0.1%
11.12385363 1
0.1%
11.09790359 1
0.1%
11.02146142 1
0.1%
10.92645121 1
0.1%
10.89236353 1
0.1%

contrast
Real number (ℝ)

Distinct758
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean474.80716
Minimum0
Maximum1247.9789
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2022-12-06T15:15:32.649566image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186.46742
Q1340.74456
median461.86926
Q3587.07306
95-th percentile803.91829
Maximum1247.9789
Range1247.9789
Interquartile range (IQR)246.32851

Descriptive statistics

Standard deviation186.46566
Coefficient of variation (CV)0.39271872
Kurtosis0.45992295
Mean474.80716
Median Absolute Deviation (MAD)122.87535
Skewness0.42567724
Sum372723.62
Variance34769.443
MonotonicityNot monotonic
2022-12-06T15:15:32.818822image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.5%
173.15457 3
 
0.4%
386.3307758 2
 
0.3%
716.8315443 2
 
0.3%
319.9546875 2
 
0.3%
578.9932057 2
 
0.3%
295.6501976 2
 
0.3%
364.1111434 2
 
0.3%
436.7449366 2
 
0.3%
318.1995208 2
 
0.3%
Other values (748) 762
97.1%
ValueCountFrequency (%)
0 4
0.5%
31.51516443 1
 
0.1%
50.64383606 1
 
0.1%
51.48035341 1
 
0.1%
91.46387145 1
 
0.1%
96.84072825 1
 
0.1%
109.4206903 1
 
0.1%
112.7492185 1
 
0.1%
122.7049653 1
 
0.1%
125.1393216 1
 
0.1%
ValueCountFrequency (%)
1247.978855 1
0.1%
1044.508138 1
0.1%
1039.331602 1
0.1%
1022.113275 1
0.1%
1001.51685 1
0.1%
995.4271552 1
0.1%
994.7034604 1
0.1%
990.4930055 1
0.1%
989.733292 1
0.1%
985.7055013 1
0.1%

homogeneity
Real number (ℝ)

Distinct758
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32926185
Minimum0
Maximum0.55062789
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2022-12-06T15:15:32.981559image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.27512579
Q10.30007522
median0.31649205
Q30.34444409
95-th percentile0.44951645
Maximum0.55062789
Range0.55062789
Interquartile range (IQR)0.044368872

Descriptive statistics

Standard deviation0.056425953
Coefficient of variation (CV)0.17137106
Kurtosis7.3543214
Mean0.32926185
Median Absolute Deviation (MAD)0.020369186
Skewness0.31060125
Sum258.47056
Variance0.0031838882
MonotonicityNot monotonic
2022-12-06T15:15:33.150798image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.5%
0.2491941472 3
 
0.4%
0.3209741237 2
 
0.3%
0.3992840112 2
 
0.3%
0.3185537618 2
 
0.3%
0.5254654715 2
 
0.3%
0.3186987383 2
 
0.3%
0.3168260033 2
 
0.3%
0.2994443101 2
 
0.3%
0.3955498702 2
 
0.3%
Other values (748) 762
97.1%
ValueCountFrequency (%)
0 4
0.5%
0.2133352985 1
 
0.1%
0.2180936544 1
 
0.1%
0.2452939561 1
 
0.1%
0.2490866717 1
 
0.1%
0.2491941472 3
0.4%
0.2526814058 1
 
0.1%
0.2530573272 1
 
0.1%
0.2531765926 1
 
0.1%
0.2559761421 1
 
0.1%
ValueCountFrequency (%)
0.550627886 1
0.1%
0.5443551063 1
0.1%
0.5440113543 1
0.1%
0.5254654715 2
0.3%
0.5211341595 1
0.1%
0.517781856 1
0.1%
0.5139849153 1
0.1%
0.5109200297 1
0.1%
0.51046677 1
0.1%
0.5016485121 1
0.1%

energy
Real number (ℝ)

Distinct758
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11218233
Minimum0
Maximum0.19780022
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2022-12-06T15:15:33.335693image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.092597773
Q10.10574237
median0.11352701
Q30.1196374
95-th percentile0.12997487
Maximum0.19780022
Range0.19780022
Interquartile range (IQR)0.013895029

Descriptive statistics

Standard deviation0.015565551
Coefficient of variation (CV)0.13875225
Kurtosis15.691394
Mean0.11218233
Median Absolute Deviation (MAD)0.0066885886
Skewness-1.1822805
Sum88.063131
Variance0.00024228637
MonotonicityNot monotonic
2022-12-06T15:15:33.504935image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.5%
0.06974391534 3
 
0.4%
0.1208751608 2
 
0.3%
0.1124587111 2
 
0.3%
0.1222393615 2
 
0.3%
0.1207371207 2
 
0.3%
0.101911949 2
 
0.3%
0.1213163643 2
 
0.3%
0.09974837679 2
 
0.3%
0.1170753861 2
 
0.3%
Other values (748) 762
97.1%
ValueCountFrequency (%)
0 4
0.5%
0.06974391534 3
0.4%
0.07248878898 1
 
0.1%
0.07694684682 1
 
0.1%
0.07881743908 1
 
0.1%
0.08211268138 1
 
0.1%
0.08222390264 1
 
0.1%
0.08910883864 1
 
0.1%
0.08948773315 1
 
0.1%
0.08964066853 1
 
0.1%
ValueCountFrequency (%)
0.1978002178 1
0.1%
0.1898747234 1
0.1%
0.184837922 1
0.1%
0.1707014153 1
0.1%
0.1648860816 1
0.1%
0.1647475409 1
0.1%
0.1625575716 1
0.1%
0.1610145276 1
0.1%
0.1584899859 1
0.1%
0.1559152029 1
0.1%

ASM
Real number (ℝ)

Distinct758
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012826853
Minimum0
Maximum0.039124926
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2022-12-06T15:15:33.683371image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0085743491
Q10.011181448
median0.012888382
Q30.014313106
95-th percentile0.016893539
Maximum0.039124926
Range0.039124926
Interquartile range (IQR)0.0031316583

Descriptive statistics

Standard deviation0.0033513892
Coefficient of variation (CV)0.26127914
Kurtosis12.161643
Mean0.012826853
Median Absolute Deviation (MAD)0.0015198402
Skewness1.7738681
Sum10.06908
Variance1.123181 × 10-5
MonotonicityNot monotonic
2022-12-06T15:15:33.852690image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.5%
0.004864213727 3
 
0.4%
0.01461080451 2
 
0.3%
0.0126469617 2
 
0.3%
0.01494246149 2
 
0.3%
0.0145774523 2
 
0.3%
0.01038604536 2
 
0.3%
0.01471766025 2
 
0.3%
0.009949738672 2
 
0.3%
0.01370664602 2
 
0.3%
Other values (748) 762
97.1%
ValueCountFrequency (%)
0 4
0.5%
0.004864213727 3
0.4%
0.005254624528 1
 
0.1%
0.005920817236 1
 
0.1%
0.006212188703 1
 
0.1%
0.006742492444 1
 
0.1%
0.006760770165 1
 
0.1%
0.007940385124 1
 
0.1%
0.008008054385 1
 
0.1%
0.008035449454 1
 
0.1%
ValueCountFrequency (%)
0.03912492616 1
0.1%
0.03605241059 1
0.1%
0.0341650574 1
0.1%
0.0291389732 1
0.1%
0.0271874199 1
0.1%
0.02714175224 1
0.1%
0.0264249641 1
0.1%
0.02592567811 1
0.1%
0.02511907562 1
0.1%
0.0243095505 1
0.1%

correlation
Real number (ℝ)

Distinct758
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89378731
Minimum0
Maximum0.98014047
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size28.4 KiB
2022-12-06T15:15:34.037670image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.85819364
Q10.88538728
median0.901378
Q30.91308046
95-th percentile0.92967496
Maximum0.98014047
Range0.98014047
Interquartile range (IQR)0.027693175

Descriptive statistics

Standard deviation0.067711141
Coefficient of variation (CV)0.075757555
Kurtosis153.11835
Mean0.89378731
Median Absolute Deviation (MAD)0.013192236
Skewness-11.763434
Sum701.62303
Variance0.0045847986
MonotonicityNot monotonic
2022-12-06T15:15:34.200403image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.5%
0.9327378386 3
 
0.4%
0.9103469797 2
 
0.3%
0.9006620357 2
 
0.3%
0.9129601956 2
 
0.3%
0.8929623335 2
 
0.3%
0.8714064423 2
 
0.3%
0.9159487527 2
 
0.3%
0.8367871068 2
 
0.3%
0.9166136626 2
 
0.3%
Other values (748) 762
97.1%
ValueCountFrequency (%)
0 4
0.5%
0.8072905861 1
 
0.1%
0.8107247302 1
 
0.1%
0.8202676524 1
 
0.1%
0.8214569153 1
 
0.1%
0.8239009581 1
 
0.1%
0.8293722941 1
 
0.1%
0.8323755886 1
 
0.1%
0.8367871068 2
0.3%
0.8380641288 1
 
0.1%
ValueCountFrequency (%)
0.9801404681 1
0.1%
0.9726470757 1
0.1%
0.9648451243 1
0.1%
0.9517081469 1
0.1%
0.9487090682 1
0.1%
0.9472151019 1
0.1%
0.9432368566 1
0.1%
0.9413040682 1
0.1%
0.9393905008 1
0.1%
0.9383601088 1
0.1%

Label
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0.0
394 
2.0
131 
1.0
130 
3.0
130 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2355
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 394
50.2%
2.0 131
 
16.7%
1.0 130
 
16.6%
3.0 130
 
16.6%

Length

2022-12-06T15:15:34.338421image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-06T15:15:34.485535image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 394
50.2%
2.0 131
 
16.7%
1.0 130
 
16.6%
3.0 130
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 1179
50.1%
. 785
33.3%
2 131
 
5.6%
1 130
 
5.5%
3 130
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1570
66.7%
Other Punctuation 785
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1179
75.1%
2 131
 
8.3%
1 130
 
8.3%
3 130
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 785
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2355
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1179
50.1%
. 785
33.3%
2 131
 
5.6%
1 130
 
5.5%
3 130
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1179
50.1%
. 785
33.3%
2 131
 
5.6%
1 130
 
5.5%
3 130
 
5.5%

Interactions

2022-12-06T15:15:30.756042image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:25.907227image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:26.846029image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:27.770538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:28.688450image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:29.769117image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:30.909656image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:26.056387image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:26.999715image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:27.917656image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:28.888932image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:29.938362image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:31.094524image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:26.213506image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:27.146896image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:28.071331image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:29.089421image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:30.107605image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:31.241699image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:26.367184image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:27.284868image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:28.202827image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:29.274282image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:30.270330image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:31.410948image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:26.529922image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:27.447592image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:28.372096image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:29.437016image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:30.439686image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:31.567990image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:26.698941image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:27.616826image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:28.534830image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:29.606324image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-12-06T15:15:30.608898image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2022-12-06T15:15:34.601405image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-06T15:15:34.786307image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-06T15:15:34.955632image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-06T15:15:35.124938image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-06T15:15:35.309796image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-06T15:15:31.758358image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-06T15:15:31.943251image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

dissimilaritycontrasthomogeneityenergyASMcorrelationLabel
89.763750601.1221160.2783760.0993820.0098770.8858220.0
4178.143442552.2344930.3091900.0983140.0096660.8683111.0
2388.271145525.0908510.3414980.1625580.0264250.8581010.0
1508.916525605.1204560.3158090.1107360.0122620.8926500.0
6028.836776371.5241740.2831340.1211410.0146750.9141242.0
1908.404615462.6700060.3051700.1154200.0133220.9144610.0
4907.739550426.0013500.3366770.0995110.0099020.8912161.0
7478.314785616.9652150.3299570.1115550.0124440.9092423.0
5448.789448636.6205730.3425180.1438120.0206820.8942302.0
22010.312440652.7649320.2875850.1460740.0213380.8955560.0
dissimilaritycontrasthomogeneityenergyASMcorrelationLabel
388.671381563.1534140.3132150.1107030.0122550.8939780.0
2329.522023562.8800020.2902620.1107530.0122660.8979810.0
788.706890493.0813120.3043550.1169350.0136740.8917620.0
4498.1364611022.1132750.4615850.1271290.0161620.8542861.0
4187.169650600.1434940.3980210.1109490.0123100.8890371.0
1667.740790660.4607910.3791410.1082450.0117170.8803210.0
1828.630151332.9010180.2765130.1166430.0136060.9266420.0
6676.372181198.1705870.3305870.1263820.0159730.9302583.0
5679.063893361.3343600.2705860.0949260.0090110.9164462.0
4667.189349461.8692550.3534830.1194830.0142760.8603021.0

Duplicate rows

Most frequently occurring

dissimilaritycontrasthomogeneityenergyASMcorrelationLabel# duplicates
00.0000000.0000000.0000000.0000000.0000000.0000000.04
87.173842173.1545700.2491940.0697440.0048640.9327380.03
16.003810213.4898090.3365040.0998680.0099740.9163690.02
26.208472272.3551690.3640360.1253390.0157100.9199973.02
36.314178174.1679200.2947950.0909730.0082760.9315583.02
46.365173578.9932060.5254650.1207370.0145770.8929621.02
56.426393318.1995210.3955500.1170750.0137070.9166142.02
67.062337579.2240690.4180780.1123300.0126180.8930281.02
77.172234338.4067400.3263580.1221270.0149150.9247150.02
97.367757295.6501980.3186990.1019120.0103860.8714060.02